{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3045e56c",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "source": [
    "# Fully-Connected Neural Nets\n",
    "In this exercise we will implement fully-connected networks using a modular approach. For each layer we will implement a `forward` and a `backward` function. The `forward` function will receive inputs, weights, and other parameters and will return both an output and a `cache` object storing data needed for the backward pass, like this:\n",
    "\n",
    "```python\n",
    "def layer_forward(x, w):\n",
    "  \"\"\" Receive inputs x and weights w \"\"\"\n",
    "  # Do some computations ...\n",
    "  z = # ... some intermediate value\n",
    "  # Do some more computations ...\n",
    "  out = # the output\n",
    "   \n",
    "  cache = (x, w, z, out) # Values we need to compute gradients\n",
    "   \n",
    "  return out, cache\n",
    "```\n",
    "\n",
    "The backward pass will receive upstream derivatives and the `cache` object, and will return gradients with respect to the inputs and weights, like this:\n",
    "\n",
    "```python\n",
    "def layer_backward(dout, cache):\n",
    "  \"\"\"\n",
    "  Receive dout (derivative of loss with respect to outputs) and cache,\n",
    "  and compute derivative with respect to inputs.\n",
    "  \"\"\"\n",
    "  # Unpack cache values\n",
    "  x, w, z, out = cache\n",
    "  \n",
    "  # Use values in cache to compute derivatives\n",
    "  dx = # Derivative of loss with respect to x\n",
    "  dw = # Derivative of loss with respect to w\n",
    "  \n",
    "  return dx, dw\n",
    "```\n",
    "\n",
    "After implementing a bunch of layers this way, we will be able to easily combine them to build classifiers with different architectures.\n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd40e03b",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "# As usual, a bit of setup\n",
    "from __future__ import print_function\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cs231n.classifiers.fc_net import *\n",
    "from cs231n.data_utils import get_CIFAR10_data\n",
    "from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array\n",
    "from cs231n.solver import Solver\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "# for auto-reloading external modules\n",
    "# see http://stackoverflow.com/questions/1907993/autoreload-of-modules-in-ipython\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "  \"\"\" returns relative error \"\"\"\n",
    "  return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1b6ca973",
   "metadata": {
    "tags": [
     "pdf-ignore"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('X_train: ', (49000, 3, 32, 32))\n",
      "('y_train: ', (49000,))\n",
      "('X_val: ', (1000, 3, 32, 32))\n",
      "('y_val: ', (1000,))\n",
      "('X_test: ', (1000, 3, 32, 32))\n",
      "('y_test: ', (1000,))\n"
     ]
    }
   ],
   "source": [
    "# Load the (preprocessed) CIFAR10 data.\n",
    "\n",
    "data = get_CIFAR10_data(cifar10_dir='../Dataset/CIFAR10')\n",
    "for k, v in list(data.items()):\n",
    "  print(('%s: ' % k, v.shape))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a527a66e",
   "metadata": {},
   "source": [
    "# Affine layer: forward\n",
    "Open the file `cs231n/layers.py` and implement the `affine_forward` function.\n",
    "\n",
    "Once you are done you can test your implementaion by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "26b58b5a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_forward function:\n",
      "difference:  9.769849468192957e-10\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_forward function\n",
    "\n",
    "num_inputs = 2\n",
    "input_shape = (4, 5, 6)\n",
    "output_dim = 3\n",
    "\n",
    "input_size = num_inputs * np.prod(input_shape)\n",
    "weight_size = output_dim * np.prod(input_shape)\n",
    "\n",
    "x = np.linspace(-0.1, 0.5, num=input_size).reshape(num_inputs, *input_shape)\n",
    "w = np.linspace(-0.2, 0.3, num=weight_size).reshape(np.prod(input_shape), output_dim)\n",
    "b = np.linspace(-0.3, 0.1, num=output_dim)\n",
    "\n",
    "out, _ = affine_forward(x, w, b)\n",
    "correct_out = np.array([[ 1.49834967,  1.70660132,  1.91485297],\n",
    "                        [ 3.25553199,  3.5141327,   3.77273342]])\n",
    "\n",
    "# Compare your output with ours. The error should be around e-9 or less.\n",
    "print('Testing affine_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9ea1388",
   "metadata": {},
   "source": [
    "# Affine layer: backward\n",
    "Now implement the `affine_backward` function and test your implementation using numeric gradient checking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "da91c872",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_backward function:\n",
      "dx error:  5.399100368651805e-11\n",
      "dw error:  9.904211865398145e-11\n",
      "db error:  2.4122867568119087e-11\n"
     ]
    }
   ],
   "source": [
    "# Test the affine_backward function\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 2, 3)\n",
    "w = np.random.randn(6, 5)\n",
    "b = np.random.randn(5)\n",
    "dout = np.random.randn(10, 5)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "_, cache = affine_forward(x, w, b)\n",
    "dx, dw, db = affine_backward(dout, cache)\n",
    "\n",
    "# The error should be around e-10 or less\n",
    "print('Testing affine_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c270c0b",
   "metadata": {},
   "source": [
    "# ReLU activation: forward\n",
    "Implement the forward pass for the ReLU activation function in the `relu_forward` function and test your implementation using the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "73ea11eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_forward function:\n",
      "difference:  4.999999798022158e-08\n"
     ]
    }
   ],
   "source": [
    "# Test the relu_forward function\n",
    "\n",
    "x = np.linspace(-0.5, 0.5, num=12).reshape(3, 4)\n",
    "\n",
    "out, _ = relu_forward(x)\n",
    "correct_out = np.array([[ 0.,          0.,          0.,          0.,        ],\n",
    "                        [ 0.,          0.,          0.04545455,  0.13636364,],\n",
    "                        [ 0.22727273,  0.31818182,  0.40909091,  0.5,       ]])\n",
    "\n",
    "# Compare your output with ours. The error should be on the order of e-8\n",
    "print('Testing relu_forward function:')\n",
    "print('difference: ', rel_error(out, correct_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed1e97af",
   "metadata": {},
   "source": [
    "# ReLU activation: backward\n",
    "Now implement the backward pass for the ReLU activation function in the `relu_backward` function and test your implementation using numeric gradient checking:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4852deab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing relu_backward function:\n",
      "dx error:  3.2756349136310288e-12\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "x = np.random.randn(10, 10)\n",
    "dout = np.random.randn(*x.shape)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: relu_forward(x)[0], x, dout)\n",
    "\n",
    "_, cache = relu_forward(x)\n",
    "dx = relu_backward(dout, cache)\n",
    "\n",
    "# The error should be on the order of e-12\n",
    "print('Testing relu_backward function:')\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "2116f9e2",
   "metadata": {
    "tags": [
     "pdf-inline"
    ]
   },
   "source": [
    "## Inline Question 1: \n",
    "\n",
    "We've only asked you to implement ReLU, but there are a number of different activation functions that one could use in neural networks, each with its pros and cons. In particular, an issue commonly seen with activation functions is getting zero (or close to zero) gradient flow during backpropagation. Which of the following activation functions have this problem? If you consider these functions in the one dimensional case, what types of input would lead to this behaviour?\n",
    "1. Sigmoid\n",
    "2. ReLU\n",
    "3. Leaky ReLU\n",
    "\n",
    "## Answer:\n",
    "\n",
    "1. Sigmoid function suffers from the vanishing gradient problem because the gradient takes small values in the tails of the function (close to 0). A one dimensional example is to saturate the function, e.g., [-1e5,1e5]\n",
    "\n",
    "2. ReLU's gradient is 0 or 1. Thus it can suffer from the vanishing gradient problem when all input values are negative. However, having all negative inputs is less probable. Because of this problem occurs only in one side (negative values), it is also known as the \"dying ReLU problem\", this name comes from the fact that some neurons will never be updated, i.e., they will be \"dead\". A one dimensional example that can vanish the gradient is to consider only negative values, e.g., [-1,-2,-4,-5].\n",
    "\n",
    "3. Leaky ReLU tries to solve the ReLU problem of \"dead\" neurons by considering small negative slope when we have negative values, i.e., if x < 0 then alpha else x, where alpha can be equal to 0.01. A one dimensional example that can vanish the gradient is to consider all zero values, e.g., [0,0,0,0], it can only happen with a bad network initialization.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8cdb366",
   "metadata": {},
   "source": [
    "# \"Sandwich\" layers\n",
    "There are some common patterns of layers that are frequently used in neural nets. For example, affine layers are frequently followed by a ReLU nonlinearity. To make these common patterns easy, we define several convenience layers in the file `cs231n/layer_utils.py`.\n",
    "\n",
    "For now take a look at the `affine_relu_forward` and `affine_relu_backward` functions, and run the following to numerically gradient check the backward pass:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0ce1580a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing affine_relu_forward and affine_relu_backward:\n",
      "dx error:  2.299579177309368e-11\n",
      "dw error:  8.162011105764925e-11\n",
      "db error:  7.826724021458994e-12\n"
     ]
    }
   ],
   "source": [
    "from cs231n.layer_utils import affine_relu_forward, affine_relu_backward\n",
    "np.random.seed(231)\n",
    "x = np.random.randn(2, 3, 4)\n",
    "w = np.random.randn(12, 10)\n",
    "b = np.random.randn(10)\n",
    "dout = np.random.randn(2, 10)\n",
    "\n",
    "out, cache = affine_relu_forward(x, w, b)\n",
    "dx, dw, db = affine_relu_backward(dout, cache)\n",
    "\n",
    "dx_num = eval_numerical_gradient_array(lambda x: affine_relu_forward(x, w, b)[0], x, dout)\n",
    "dw_num = eval_numerical_gradient_array(lambda w: affine_relu_forward(x, w, b)[0], w, dout)\n",
    "db_num = eval_numerical_gradient_array(lambda b: affine_relu_forward(x, w, b)[0], b, dout)\n",
    "\n",
    "# Relative error should be around e-10 or less\n",
    "print('Testing affine_relu_forward and affine_relu_backward:')\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "print('dw error: ', rel_error(dw_num, dw))\n",
    "print('db error: ', rel_error(db_num, db))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caf21167",
   "metadata": {},
   "source": [
    "# Loss layers: Softmax and SVM\n",
    "Now implement the loss and gradient for softmax and SVM in the `softmax_loss` and `svm_loss` function in `cs231n/layers.py`. These should be similar to what you implemented in `cs231n/classifiers/softmax.py` and `cs231n/classifiers/linear_svm.py`.\n",
    "\n",
    "You can make sure that the implementations are correct by running the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e14d422b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing svm_loss:\n",
      "loss:  8.999602749096233\n",
      "dx error:  1.4021566006651672e-09\n",
      "\n",
      "Testing softmax_loss:\n",
      "loss:  2.302545844500738\n",
      "dx error:  9.384673161989355e-09\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "num_classes, num_inputs = 10, 50\n",
    "x = 0.001 * np.random.randn(num_inputs, num_classes)\n",
    "y = np.random.randint(num_classes, size=num_inputs)\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: svm_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = svm_loss(x, y)\n",
    "\n",
    "# Test svm_loss function. Loss should be around 9 and dx error should be around the order of e-9\n",
    "print('Testing svm_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))\n",
    "\n",
    "dx_num = eval_numerical_gradient(lambda x: softmax_loss(x, y)[0], x, verbose=False)\n",
    "loss, dx = softmax_loss(x, y)\n",
    "\n",
    "# Test softmax_loss function. Loss should be close to 2.3 and dx error should be around e-8\n",
    "print('\\nTesting softmax_loss:')\n",
    "print('loss: ', loss)\n",
    "print('dx error: ', rel_error(dx_num, dx))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f739e24",
   "metadata": {},
   "source": [
    "# Two-layer network\n",
    "Open the file `cs231n/classifiers/fc_net.py` and complete the implementation of the `TwoLayerNet` class. Read through it to make sure you understand the API. You can run the cell below to test your implementation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b8ed0018",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing initialization ... \n",
      "Testing test-time forward pass ... \n",
      "Testing training loss (no regularization)\n",
      "Running numeric gradient check with reg =  0.0\n",
      "W1 relative error: 1.83e-08\n",
      "W2 relative error: 3.12e-10\n",
      "b1 relative error: 9.83e-09\n",
      "b2 relative error: 4.33e-10\n",
      "Running numeric gradient check with reg =  0.7\n",
      "W1 relative error: 2.53e-07\n",
      "W2 relative error: 2.85e-08\n",
      "b1 relative error: 1.56e-08\n",
      "b2 relative error: 7.76e-10\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(231)\n",
    "N, D, H, C = 3, 5, 50, 7\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=N)\n",
    "\n",
    "std = 1e-3\n",
    "model = TwoLayerNet(input_dim=D, hidden_dim=H, num_classes=C, weight_scale=std)\n",
    "\n",
    "print('Testing initialization ... ')\n",
    "W1_std = abs(model.params['W1'].std() - std)\n",
    "b1 = model.params['b1']\n",
    "W2_std = abs(model.params['W2'].std() - std)\n",
    "b2 = model.params['b2']\n",
    "assert W1_std < std / 10, 'First layer weights do not seem right'\n",
    "assert np.all(b1 == 0), 'First layer biases do not seem right'\n",
    "assert W2_std < std / 10, 'Second layer weights do not seem right'\n",
    "assert np.all(b2 == 0), 'Second layer biases do not seem right'\n",
    "\n",
    "print('Testing test-time forward pass ... ')\n",
    "model.params['W1'] = np.linspace(-0.7, 0.3, num=D*H).reshape(D, H)\n",
    "model.params['b1'] = np.linspace(-0.1, 0.9, num=H)\n",
    "model.params['W2'] = np.linspace(-0.3, 0.4, num=H*C).reshape(H, C)\n",
    "model.params['b2'] = np.linspace(-0.9, 0.1, num=C)\n",
    "X = np.linspace(-5.5, 4.5, num=N*D).reshape(D, N).T\n",
    "scores = model.loss(X)\n",
    "correct_scores = np.asarray(\n",
    "  [[11.53165108,  12.2917344,   13.05181771,  13.81190102,  14.57198434, 15.33206765,  16.09215096],\n",
    "   [12.05769098,  12.74614105,  13.43459113,  14.1230412,   14.81149128, 15.49994135,  16.18839143],\n",
    "   [12.58373087,  13.20054771,  13.81736455,  14.43418138,  15.05099822, 15.66781506,  16.2846319 ]])\n",
    "scores_diff = np.abs(scores - correct_scores).sum()\n",
    "assert scores_diff < 1e-6, 'Problem with test-time forward pass'\n",
    "\n",
    "print('Testing training loss (no regularization)')\n",
    "y = np.asarray([0, 5, 1])\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 3.4702243556\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with training-time loss'\n",
    "\n",
    "model.reg = 1.0\n",
    "loss, grads = model.loss(X, y)\n",
    "correct_loss = 26.5948426952\n",
    "assert abs(loss - correct_loss) < 1e-10, 'Problem with regularization loss'\n",
    "\n",
    "# Errors should be around e-7 or less\n",
    "for reg in [0.0, 0.7]:\n",
    "  print('Running numeric gradient check with reg = ', reg)\n",
    "  model.reg = reg\n",
    "  loss, grads = model.loss(X, y)\n",
    "\n",
    "  for name in sorted(grads):\n",
    "    f = lambda _: model.loss(X, y)[0]\n",
    "    grad_num = eval_numerical_gradient(f, model.params[name], verbose=False)\n",
    "    print('%s relative error: %.2e' % (name, rel_error(grad_num, grads[name])))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f67545",
   "metadata": {},
   "source": [
    "# Solver\n",
    "Open the file `cs231n/solver.py` and read through it to familiarize yourself with the API. You also need to implement the `sgd` function in `cs231n/optim.py`. After doing so, use a `Solver` instance to train a `TwoLayerNet` that achieves about `36%` accuracy on the validation set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cf423ed9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lr: 0.0015403103441032713 reg: 6.168711109084249e-05 h: 65 val acc: 0.487\n",
      "lr: 0.00021000482291162197 reg: 4.075146143922161e-05 h: 129 val acc: 0.483\n",
      "lr: 0.0006029476804350068 reg: 3.122517998321929e-06 h: 164 val acc: 0.493\n",
      "lr: 0.001777811942911416 reg: 1.4567057103372342e-06 h: 171 val acc: 0.499\n",
      "lr: 0.0007449527558633125 reg: 1.2724229443487698e-05 h: 25 val acc: 0.47\n",
      "lr: 0.0014373596875184842 reg: 7.067401021237151e-07 h: 10 val acc: 0.424\n",
      "lr: 0.00013469664309246818 reg: 1.2271385894539518e-06 h: 69 val acc: 0.427\n",
      "lr: 0.00034083233065795897 reg: 4.4279992471935126e-07 h: 49 val acc: 0.459\n",
      "lr: 0.0001341361773180545 reg: 7.571661326053832e-05 h: 69 val acc: 0.433\n",
      "lr: 0.003630034531245389 reg: 1.063280349181885e-06 h: 97 val acc: 0.328\n",
      "lr: 0.0005326229857994681 reg: 1.4610771619393725e-07 h: 92 val acc: 0.501\n",
      "lr: 0.0023312665094660703 reg: 5.158717187270773e-06 h: 193 val acc: 0.489\n",
      "lr: 0.0064677783368340454 reg: 3.631999981761626e-06 h: 164 val acc: 0.249\n",
      "lr: 0.0001286667238551478 reg: 1.994608402166181e-05 h: 164 val acc: 0.437\n",
      "lr: 0.001404381708338706 reg: 3.8435052593241056e-06 h: 21 val acc: 0.453\n",
      "lr: 0.00010521308271932793 reg: 3.0637813937923057e-07 h: 109 val acc: 0.423\n",
      "lr: 0.00013133838024979516 reg: 6.31999534780451e-07 h: 55 val acc: 0.418\n",
      "lr: 0.007867004486224584 reg: 7.821297830835608e-05 h: 13 val acc: 0.154\n",
      "lr: 0.003635366032090501 reg: 5.2969121631042505e-06 h: 105 val acc: 0.276\n",
      "lr: 0.008031954280796758 reg: 1.545133309297152e-07 h: 12 val acc: 0.149\n",
      "lr: 0.0027050518051914834 reg: 6.203995918310556e-06 h: 166 val acc: 0.406\n",
      "lr: 0.005297683809089099 reg: 5.549438455762553e-05 h: 63 val acc: 0.213\n",
      "lr: 0.0049074083115226855 reg: 1.9171073247103627e-05 h: 100 val acc: 0.302\n",
      "lr: 0.000359583875774351 reg: 3.279411721826441e-06 h: 188 val acc: 0.5\n",
      "lr: 0.0024726911125235633 reg: 4.93692413740968e-05 h: 189 val acc: 0.477\n",
      "lr: 0.00023116914313697242 reg: 1.345764463566646e-07 h: 106 val acc: 0.472\n",
      "lr: 0.00638665947465676 reg: 5.13332163206463e-05 h: 15 val acc: 0.192\n",
      "lr: 0.0009587569074533627 reg: 2.6971393960207837e-07 h: 117 val acc: 0.496\n",
      "lr: 0.0007078530599402822 reg: 1.0037989784324179e-06 h: 131 val acc: 0.508\n",
      "lr: 0.0028386566736173535 reg: 6.509958544360264e-07 h: 14 val acc: 0.401\n",
      "Best val acc: 0.508\n"
     ]
    }
   ],
   "source": [
    "input_size = 32 * 32 * 3\n",
    "hidden_size = 50\n",
    "num_classes = 10\n",
    "model = TwoLayerNet(input_size, hidden_size, num_classes)\n",
    "solver = None\n",
    "best_model = None \n",
    "\n",
    "##############################################################################\n",
    "# TODO: Use a Solver instance to train a TwoLayerNet that achieves about 36% #\n",
    "# accuracy on the validation set.                                            #\n",
    "##############################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "best_val = -1 \n",
    "\n",
    "def random_hyperparams(lr_min, lr_max, reg_min, reg_max, h_min, h_max): \n",
    "    lr = 10**np.random.uniform(lr_min, lr_max) \n",
    "    reg = 10**np.random.uniform(reg_min, reg_max) \n",
    "    hidden = np.random.randint(h_min, h_max) \n",
    "    return lr, reg, hidden \n",
    "\n",
    "for i in range(30): \n",
    "    lr, reg, hidden_size = random_hyperparams(-4,-2, -7,-4, 10,200) \n",
    "    model = TwoLayerNet(hidden_dim=hidden_size, reg=reg) \n",
    "    _solver = Solver(model, data, update_rule=\"sgd\", optim_config={\"learning_rate\": lr}, \n",
    "                    lr_decay=0.95, num_epochs=5, batch_size=128, print_every=-1, verbose=False) \n",
    "    _solver.train() \n",
    "    val_accuracy = _solver.best_val_acc \n",
    "    if best_val < val_accuracy: \n",
    "        best_val = val_accuracy \n",
    "        solver = _solver \n",
    "        best_model = model \n",
    "    \n",
    "    print(\"lr: {} reg: {} h: {} val acc: {}\".format(lr, reg, hidden_size, val_accuracy)) \n",
    "\n",
    "print(\"Best val acc:\", best_val) \n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "##############################################################################\n",
    "#                             END OF YOUR CODE                               #\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1245160",
   "metadata": {},
   "source": [
    "# Debug the training\n",
    "With the default parameters we provided above, you should get a validation accuracy of about 0.36 on the validation set. This isn't very good.\n",
    "\n",
    "One strategy for getting insight into what's wrong is to plot the loss function and the accuracies on the training and validation sets during optimization.\n",
    "\n",
    "Another strategy is to visualize the weights that were learned in the first layer of the network. In most neural networks trained on visual data, the first layer weights typically show some visible structure when visualized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1788b1a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run this cell to visualize training loss and train / val accuracy\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(solver.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(solver.train_acc_history, '-o', label='train')\n",
    "plt.plot(solver.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(solver.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9ddfe290",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from cs231n.vis_utils import visualize_grid\n",
    "\n",
    "# Visualize the weights of the network\n",
    "\n",
    "def show_net_weights(net):\n",
    "    W1 = net.params['W1']\n",
    "    W1 = W1.reshape(3, 32, 32, -1).transpose(3, 1, 2, 0)\n",
    "    plt.imshow(visualize_grid(W1, padding=3).astype('uint8'))\n",
    "    plt.gca().axis('off')\n",
    "    plt.show()\n",
    "\n",
    "show_net_weights(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d2a0395",
   "metadata": {},
   "source": [
    "# Tune your hyperparameters\n",
    "\n",
    "**What's wrong?**. Looking at the visualizations above, we see that the loss is decreasing more or less linearly, which seems to suggest that the learning rate may be too low. Moreover, there is no gap between the training and validation accuracy, suggesting that the model we used has low capacity, and that we should increase its size. On the other hand, with a very large model we would expect to see more overfitting, which would manifest itself as a very large gap between the training and validation accuracy.\n",
    "\n",
    "**Tuning**. Tuning the hyperparameters and developing intuition for how they affect the final performance is a large part of using Neural Networks, so we want you to get a lot of practice. Below, you should experiment with different values of the various hyperparameters, including hidden layer size, learning rate, numer of training epochs, and regularization strength. You might also consider tuning the learning rate decay, but you should be able to get good performance using the default value.\n",
    "\n",
    "**Approximate results**. You should be aim to achieve a classification accuracy of greater than 48% on the validation set. Our best network gets over 52% on the validation set.\n",
    "\n",
    "**Experiment**: You goal in this exercise is to get as good of a result on CIFAR-10 as you can (52% could serve as a reference), with a fully-connected Neural Network. Feel free implement your own techniques (e.g. PCA to reduce dimensionality, or adding dropout, or adding features to the solver, etc.)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d71f821d",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# best_model = None\n",
    "\n",
    "\n",
    "#################################################################################\n",
    "# TODO: Tune hyperparameters using the validation set. Store your best trained  #\n",
    "# model in best_model.                                                          #\n",
    "#                                                                               #\n",
    "# To help debug your network, it may help to use visualizations similar to the  #\n",
    "# ones we used above; these visualizations will have significant qualitative    #\n",
    "# differences from the ones we saw above for the poorly tuned network.          #\n",
    "#                                                                               #\n",
    "# Tweaking hyperparameters by hand can be fun, but you might find it useful to  #\n",
    "# write code to sweep through possible combinations of hyperparameters          #\n",
    "# automatically like we did on thexs previous exercises.                          #\n",
    "#################################################################################\n",
    "# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "\n",
    "pass \n",
    "\n",
    "# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****\n",
    "################################################################################\n",
    "#                              END OF YOUR CODE                                #\n",
    "################################################################################"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e50c1dc",
   "metadata": {},
   "source": [
    "# Test your model!\n",
    "Run your best model on the validation and test sets. You should achieve above 48% accuracy on the validation set and the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a14cf538",
   "metadata": {
    "test": "val_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Validation set accuracy:  0.508\n"
     ]
    }
   ],
   "source": [
    "y_val_pred = np.argmax(best_model.loss(data['X_val']), axis=1)\n",
    "print('Validation set accuracy: ', (y_val_pred == data['y_val']).mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1a018fc4",
   "metadata": {
    "test": "test_accuracy"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set accuracy:  0.508\n"
     ]
    }
   ],
   "source": [
    "y_test_pred = np.argmax(best_model.loss(data['X_test']), axis=1)\n",
    "print('Test set accuracy: ', (y_test_pred == data['y_test']).mean())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9e78df2a",
   "metadata": {},
   "source": [
    "## Inline Question 2: \n",
    "\n",
    "Now that you have trained a Neural Network classifier, you may find that your testing accuracy is much lower than the training accuracy. In what ways can we decrease this gap? Select all that apply.\n",
    "\n",
    "1. Train on a larger dataset.\n",
    "2. Add more hidden units.\n",
    "3. Increase the regularization strength.\n",
    "4. None of the above.\n",
    "\n",
    "$\\color{blue}{\\textit Your Answer:}$ 1, 2, 3\n",
    "\n",
    "$\\color{blue}{\\textit Your Explanation:}$ reduces overfitting on the training data. \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "260c3fbd",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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